You're reading from The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805122500

Length 602 pages

Edition 2nd Edition

Languages

Python

Tools

MLOps

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (19) Chapters

Preface

1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER

2. Exploring ML Business Use Cases

3. Exploring ML Algorithms

4. Data Management for ML

5. Exploring Open-Source ML Libraries

6. Kubernetes Container Orchestration Infrastructure Management

7. Open-Source ML Platforms

8. Building a Data Science Environment Using AWS ML Services

9. Designing an Enterprise ML Architecture with AWS ML Services

10. Advanced ML Engineering

11. Building ML Solutions with AWS AI Services

12. AI Risk Management

13. Bias, Explainability, Privacy, and Adversarial Attacks

14. Charting the Course of Your ML Journey

15. Navigating the Generative AI Project Lifecycle

16. Designing Generative AI Platforms and Solutions

17. Other Books You May Enjoy

18. Index

Navigating the Generative AI Project Lifecycle

As briefly mentioned in Chapter 3, Exploring ML Algorithms, generative AI represents a category of AI focused on generating new data, such as text, images, videos, music, or other content, based on input data. This technology has the potential to transform numerous industries, offering capabilities previously unattainable. From entertainment to healthcare to financial services, generative AI exhibits a wide range of practical applications capable of solving intricate problems and creating innovative solutions.

In this chapter, we will embark on a practical journey, guiding you through the process of turning a generative AI project from a business concept to deployment. We will delve into the various stages of a generative AI project’s lifecycle, exploring different generative technologies, methodologies, and best practices. Specifically, we will cover the following key topics: